AIMC Topic: Deep Learning

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A two-stream convolutional neural network for microRNA transcription start site feature integration and identification.

Scientific reports
MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation and phenotype development. Understanding the regulation of miRNA genes is critical to understand gene regulation. One of the challenges to study miRNA gene regulation is ...

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nature communications
Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diver...

Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global an...

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Evaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment).

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring.

Journal of healthcare engineering
One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neura...

Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.

European radiology
OBJECTIVES: To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebra...

Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising.

NeuroImage
Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T-weighted magnetic res...

Deep learning for diagnosis of COVID-19 using 3D CT scans.

Computers in biology and medicine
A new pneumonia-type coronavirus, COVID-19, recently emerged in Wuhan, China. COVID-19 has subsequently infected many people and caused many deaths worldwide. Isolating infected people is one of the methods of preventing the spread of this virus. CT ...

Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application ...

Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning.

Sensors (Basel, Switzerland)
Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions f...